---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: BERT_Text_classification_clean
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# BERT_Text_classification_clean

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5562
- Accuracy: 0.8708
- F1: 0.8634
- Precision: 0.8673
- Recall: 0.8632

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 2.9977        | 0.07  | 50   | 2.9491          | 0.0609   | 0.0334 | 0.0689    | 0.0589 |
| 2.6422        | 0.14  | 100  | 2.1816          | 0.4223   | 0.3423 | 0.4771    | 0.4054 |
| 1.8378        | 0.21  | 150  | 1.4580          | 0.6045   | 0.5381 | 0.5940    | 0.5858 |
| 1.2155        | 0.28  | 200  | 1.0783          | 0.7019   | 0.6600 | 0.6722    | 0.6851 |
| 0.8869        | 0.35  | 250  | 0.8923          | 0.7491   | 0.7237 | 0.7474    | 0.7309 |
| 0.821         | 0.42  | 300  | 0.8259          | 0.7667   | 0.7453 | 0.7527    | 0.7494 |
| 0.7908        | 0.49  | 350  | 0.8119          | 0.7694   | 0.7417 | 0.7745    | 0.7510 |
| 0.6891        | 0.56  | 400  | 0.7503          | 0.7827   | 0.7566 | 0.7834    | 0.7659 |
| 0.6517        | 0.64  | 450  | 0.7300          | 0.7840   | 0.7592 | 0.8025    | 0.7668 |
| 0.582         | 0.71  | 500  | 0.7140          | 0.8003   | 0.7874 | 0.7946    | 0.7868 |
| 0.5354        | 0.78  | 550  | 0.7101          | 0.7789   | 0.7730 | 0.7973    | 0.7731 |
| 0.6222        | 0.85  | 600  | 0.6393          | 0.8105   | 0.7917 | 0.8066    | 0.7941 |
| 0.5159        | 0.92  | 650  | 0.6774          | 0.7946   | 0.7771 | 0.8021    | 0.7792 |
| 0.5611        | 0.99  | 700  | 0.6016          | 0.8218   | 0.8030 | 0.8211    | 0.8064 |
| 0.3468        | 1.06  | 750  | 0.6555          | 0.8113   | 0.7963 | 0.8126    | 0.7972 |
| 0.3636        | 1.13  | 800  | 0.6447          | 0.8139   | 0.8015 | 0.8182    | 0.8014 |
| 0.2689        | 1.2   | 850  | 0.5984          | 0.8332   | 0.8230 | 0.8294    | 0.8233 |
| 0.3393        | 1.27  | 900  | 0.6076          | 0.8334   | 0.8253 | 0.8337    | 0.8254 |
| 0.3395        | 1.34  | 950  | 0.5933          | 0.8364   | 0.8253 | 0.8320    | 0.8250 |
| 0.2421        | 1.41  | 1000 | 0.5973          | 0.8371   | 0.8256 | 0.8369    | 0.8254 |
| 0.2708        | 1.48  | 1050 | 0.6241          | 0.8348   | 0.8244 | 0.8311    | 0.8252 |
| 0.2972        | 1.55  | 1100 | 0.6012          | 0.8395   | 0.8292 | 0.8400    | 0.8274 |
| 0.2694        | 1.62  | 1150 | 0.6092          | 0.8425   | 0.8348 | 0.8497    | 0.8352 |
| 0.2738        | 1.69  | 1200 | 0.5839          | 0.8501   | 0.8426 | 0.8474    | 0.8414 |
| 0.2731        | 1.77  | 1250 | 0.5573          | 0.8542   | 0.8446 | 0.8491    | 0.8444 |
| 0.2472        | 1.84  | 1300 | 0.5565          | 0.8546   | 0.8476 | 0.8547    | 0.8469 |
| 0.1901        | 1.91  | 1350 | 0.5555          | 0.8586   | 0.8521 | 0.8568    | 0.8519 |
| 0.217         | 1.98  | 1400 | 0.5737          | 0.8548   | 0.8446 | 0.8549    | 0.8447 |
| 0.1559        | 2.05  | 1450 | 0.5715          | 0.8578   | 0.8494 | 0.8559    | 0.8481 |
| 0.1457        | 2.12  | 1500 | 0.5425          | 0.8650   | 0.8570 | 0.8605    | 0.8566 |
| 0.1067        | 2.19  | 1550 | 0.5734          | 0.8655   | 0.8564 | 0.8644    | 0.8560 |
| 0.1183        | 2.26  | 1600 | 0.5509          | 0.8659   | 0.8587 | 0.8617    | 0.8585 |
| 0.1507        | 2.33  | 1650 | 0.5806          | 0.8609   | 0.8528 | 0.8561    | 0.8529 |
| 0.1446        | 2.4   | 1700 | 0.5629          | 0.8700   | 0.8633 | 0.8683    | 0.8626 |
| 0.1234        | 2.47  | 1750 | 0.5690          | 0.8667   | 0.8595 | 0.8625    | 0.8595 |
| 0.0921        | 2.54  | 1800 | 0.5597          | 0.8640   | 0.8567 | 0.8599    | 0.8566 |
| 0.0866        | 2.61  | 1850 | 0.5759          | 0.8664   | 0.8593 | 0.8631    | 0.8586 |
| 0.0874        | 2.68  | 1900 | 0.5790          | 0.8680   | 0.8599 | 0.8658    | 0.8599 |
| 0.133         | 2.75  | 1950 | 0.5633          | 0.8696   | 0.8625 | 0.8674    | 0.8624 |
| 0.1309        | 2.82  | 2000 | 0.5580          | 0.8712   | 0.8640 | 0.8678    | 0.8640 |
| 0.1011        | 2.9   | 2050 | 0.5584          | 0.8711   | 0.8637 | 0.8680    | 0.8636 |
| 0.0857        | 2.97  | 2100 | 0.5562          | 0.8708   | 0.8634 | 0.8673    | 0.8632 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2